1. Introduction
The use of social networks has been growing significantly and is a popular craze today. The US and China have shaped the global social networking landscape with their ownership of influential social networks, transforming communication and society in areas like business, politics, and culture (Dixon, 2024). These social networks have not only connected people across borders but have also become powerful tools for influencing public opinion on a global scale. Their influence on society is unparalleled. In the United States, Facebook, Twitter, and Instagram have revolutionized the digital marketing industry with their advertising capabilities, and become a hub for influencers, brands, and influencing consumer behavior (Andre, 2024). With over a billion monthly active users, WeChat has become an integral part of daily life in China, as people use it for everything from chatting with friends to paying bills and ordering food. Another social network, TikTok (known as Douyin in China), has gained immense popularity globally, and viral challenges have made it a cultural phenomenon, influencing music, fashion, and internet trends worldwide (Maheshwari, 2024). Specifically, social networks are valuable tools for expanding reach, increasing brand recognition, connecting with customers, and remaining competitive in the digital realm (Kemp, 2024). By engaging with customers in real-time, businesses can foster trust and loyalty. The rise of social networks has transformed how consumers interact with brands, allowing them to share their experiences with a wide audience and netizens (Thomala, 2024). However, the rise of social networks has sparked worries about data privacy and personal information misuse, demanding further study on trust in social networks.
To maintain a positive reputation and enthrall customers worldwide, businesses must continuously seek innovative approaches and technologies to build trust. Research needs to be conducted to measure how customers will respond to or trust social networks, and if social network marketing is effective in increasing awareness (Bayne & Cianfrone, 2013; Floh et al., 2013; W. Lee & Paris, 2013; Szymczak et al., 2016). Especially, research is needed to identify the various dimensions of trust and explore the potential connections between these forms of trust and loyalty within social networks, while also investigating the possible differences in trust levels among Chinese and American customers.
This study examines trust based on affect, cognition, and institutions, and investigates if Chinese and American customers perceive these trusts differently, as well as how customers respond to marketing on social networks, and how likely they are to trust or distrust these methods and the products/companies behind them. This is important so that marketers can better adjust to societal changes and stay up to date with the latest marketing trends (Can & Kaya, 2016; J. Lee & Hong, 2016; Zhang et al., 2016), and gauge how customers trust social networks and the institutions that use social networks to market their products (Szymczak et al., 2016), and how customers’ trust significantly affects their acceptance of social network marketing (Bayne & Cianfrone, 2013; W. Lee & Paris, 2013). Finally, what managers should do to encourage customers to share their positive consumption-related experiences by offering strong arguments that will convince other customers (Floh et al., 2013; J. Lee & Hong, 2016). The findings from the data collected from China and the US will shed light on the current social network research and help managers to better position themselves on social networks.
The rest of the study is structured as follows: Literature review and hypotheses are presented in Section 2, followed by descriptions of the data, instrument, and methodology in Section 3. Empirical results and findings are discussed in Section 4. Finally, theoretical and managerial implications, as well as limitations, and recommendations for future research are highlighted in Section 5.
2. Literature Review and Hypotheses
In the world of business, establishing and maintaining customer trust is paramount for nurturing loyalty, driving sales growth, and upholding brand reputation. Long-standing customers contribute to robust, enduring relationships that are critical for sustained success. Trust and loyalty are interlinked, with loyal customers being those who trust in the business. It is essential for businesses to proactively interact with customers and address their needs to cultivate trust and loyalty.
Numerous researchers have explored the relationship between trust and loyalty, finding that commitment and satisfaction act as mediators when trust influences loyalty. For instance, Garbarino and Johnson (1999) examined the connections between satisfaction, trust, commitment, and loyalty among customers of a New York off-Broadway repertory theatre company. They observed that for low relational customers, overall satisfaction was the primary mediating factor, while for high relational customers, trust and commitment played a more significant role. Similarly, Chu et al. (2018) investigated the impact of customer satisfaction on store loyalty among Chinese consumers and discovered that the relationship was mediated by trust and commitment. Their research highlighted the robustness of the mediated relationship between customer satisfaction and loyalty among Chinese consumers compared to the direct relationship between satisfaction and loyalty. There is a considerable gap in the research regarding the segmentation of customer trust into effect-based, cognition-based, and institution-based trust and its correlation with network loyalty. Furthermore, there is a notable absence of studies that investigate the distinctions between Chinese and American customers in this regard.
Affect-based trust (ABT): McAllister (1995) states that “affect-based trust is grounded in reciprocated interpersonal care and concern” (p. 25), which is established through our understanding of others’ intentions and is confined by frequent interactions (Lewis & Weigert, 1985). Affect-based trust fosters employees’ belief that their coworkers will respond with fair social change in their relationships (Koçak, 2023).
Kyriazis et al. (2022) investigated the repercussions of a lack of affect-based trust among specialized team members on New Product Development (NPD) projects. It found that this lack of trust can result in poor communication, low cooperation, and deliberate disruptions. Despite improvements in the NPD process, negative perceptions of motives led to defensive behaviors and politics, ultimately obstructing collaboration, and impeding NPD success.
Lux et al. (2023) surveyed in Australia, 830 business professionals were involved. It was found that authentic leadership has a significant impact on positive employee outcomes, which is influenced by affective organizational commitment. The study emphasizes that personal identification and affect-based trust are the main factors driving authentic leadership, resulting in increased work engagement and job satisfaction among employees. Based on the data collected from both China and the United States, we have formulated the following hypotheses:
H1a: Social network loyalty is positively related to affect-based trust.
H1b: Chinese and American customers differ in affect-based trust.
Cognition-based trust (CBT): The “cognition-based trust is grounded in individual beliefs about peer reliability and dependability” (Mcallister, 1995, p. 25).
Koçak (2023) conducted a study using the conservation of resources theory to examine the link between interpersonal trust, prosocial motivation, psychological entitlement, and knowledge hiding. The research gathered data from 307 full-time white-collar employees in the manufacturing sector in Turkey. The results revealed that prosocial motivation served as a mediator between affect-based and cognition-based trust and evasive hiding and playing dumb, but not rationalized hiding. Additionally, psychological entitlement influenced the strength of the indirect effects of affect-based and cognition-based trust on evasive hiding and playing dumb, but not rationalized hiding through prosocial motivation.
In a study conducted in the financial sector in Turkey (Ozpamuk et al., 2023), researchers explored the correlation between trust, job satisfaction, and job performance. They gathered data from 738 employees and employed structural equation modeling to validate their hypotheses. The findings indicated that cognitive trust significantly influences job satisfaction and job performance, while affect-based trust moderately affects job satisfaction but does not have a significant impact on job performance. The study also highlighted trust as a key facilitator of knowledge exchange and effective knowledge management, which are essential for job performance. We put forth the following hypotheses related to customers engaged in social networks:
H2a: Social network loyalty is positively linked to cognition-based trust.
H2b: Chinese and American customers exhibit differences in cognition-based trust.
Institution-based trust (IBT): Institutional trust is a reciprocal relationship between an individual and an institution. It is a unique type of trust in terms of its potential impact. Pavlou and Gefen (2004) define Institution-based trust as a buyer’s perception that effective third-party institutional mechanisms are in place to facilitate transaction success. They combined sociological and economic theories about institution-based trust to propose that the perceived effectiveness of three IT-enabled institutional mechanisms - feedback mechanisms, third-party escrow services, and credit card guarantees - foster buyer trust in the community of online auction sellers.
Analysis of 274 buyers in Amazon’s online auction marketplace supports the proposed structural model. The study demonstrates that the perceived effectiveness of institutional mechanisms includes both “weak” (market-driven) and “strong” (legally binding) mechanisms. These mechanisms promote trust, not just in a few reputable sellers, but in the entire community of sellers, which contributes to an effective online marketplace.
Cheng et al. (2022) developed a theoretical model to investigate the impact of agility and network effects on value co-creation. Their findings suggest that third-party assurance mechanisms and third-party logistics play a moderating role in this relationship. The study concludes that trust based on institutions reinforces network effects by interacting with agility and multi-sided mechanisms. These conclusions provide practical guidance for online merchants to enhance value co-creation by strengthening the interaction between agility and multi-sided mechanisms, ultimately improving the social commerce platform.
Zarifis and Fu (2023) investigated the significance of trust for consumers in their decision to purchase mobile apps with advanced AI. They utilized questionnaire responses from Germany as the basis for their analysis. The research adapted established e-commerce trust models to suit this specific context and proposed a model that was tested using quantitative methods. The Structural Equation Modeling approach was employed to effectively test and validate the model. Ultimately, the findings reveal that the inclination to trust, institution-based trust, perceived sensitivity of personal information, and trust in the mobile app itself significantly impact the intention to use the mobile app with enhanced AI capabilities.
Zhao et al. (2023) analyzed the impact of financial and social certification information provided by hosts on sharing accommodation platforms, such as Airbnb, in building trust with customers. Based on data from Airbnb Beijing between January 2019 and June 2020, it was found that trust information based on institutions increases the likelihood of a purchase decision. Both financial and social certification affect customer purchase intention, and listing attributes moderate the relationship. Using different surveyed data, the following hypotheses were formulated:
H3a: Social network loyalty is positively related to institution-based trust.
H3b: Chinese and American customers differ in institution-based trust.
Social network loyalty (SNL): It is a strategic approach wherein businesses utilize social media platforms to cultivate enduring relationships with customers. The objective is to promote repeat business and brand loyalty through compelling online content and community activities. This method integrates traditional loyalty programs with social media engagement strategies, such as exclusive offers, rewards for social sharing, and interactive brand experiences, to foster a sense of belonging and appreciation among customers. By actively engaging in social media conversations, delivering valuable content, and acknowledging and rewarding loyal behaviors, companies strive to enhance customer loyalty, boost brand advocacy, and fortify their online community presence.
3. Data and Methodology
3.1. Data
The survey instruments originated from two sources. First, they were drawn from the existing literature, which included five social network experiences, twelve trusting beliefs, nineteen institution-based trusts (McKnight et al., 2002), ten affective trusts, and cognitive trust (Huang et al., 2011; Mcallister, 1995; McKnight et al., 2002; Twing-Kwong et al., 2013). Second, the authors developed twelve general social networks. The survey questionnaire was created in English using translation and back-translation techniques. These questions were crafted to delve into diverse facets of participant opinions toward social networking. Participants were asked about the frequency and duration of their social media usage, privacy issues, the types of platforms they engage with, and the purposes for which they use social networks. In addition to usage patterns and preferences, the survey aimed to gauge participants’ loyalty levels with social networking platforms. Questions were asked about their overall experience, including factors such as ease of use, reliability, and the ability to connect with others. The survey gathered cross-sectional data using a five-point Likert scale, where respondents were asked to provide their opinions on each question by selecting from “strongly agree,” “agree,” “uncertain,” “disagree,” and “strongly disagree.”
An online survey was conducted to gather responses from individuals who possess prior experience in engaging with social networks. The purpose of the survey was to gain insights into their usage patterns, preferences, and overall trust and satisfaction with social networking platforms. The survey was distributed through various online channels, including social media platforms, email lists, and online communities. Participants were encouraged to share the survey with others who met the criteria of having prior experience with social networks.
The survey targeted a diverse range of participants from both China and the United States, including individuals from different age groups, genders, education, and work experience. The aim was to ensure a representative sample that could provide a comprehensive understanding of the unique characteristics, experiences, and opinions of social media users from both countries. This allows for the analysis of cultural, social, and economic factors, providing a broader perspective on the topic being studied, and improving the study’s relevance and potential for generalization.
3.2. Methods of analysis
Methods of analysis. Correlation analysis is a statistical technique assessing the linear relationship between two or more variables, while Chi-square analysis determines the statistical significance of the differences between these variables (Hair et al., 2024; Hellebusch, 2001). Pearson correlation analysis was used to test our hypothesis about the relationship between affect-based trust, cognition-based trust, institution-based trust, and social network loyalty (Chu et al., 2018). Furthermore, this study used Chi-square analysis to compare and detect any significant differences in the trust levels between Chinese and American customers (Burns & Veeck, 2020; Odetunmibi et al., 2021). The data was analyzed with SPSS 29.0.1.0. and statistical techniques used in this study were factor analysis, correlation analysis, and Chi-square analysis.
4. Analysis and Results
The participants of this study are from China (n=960) and the United States (n=479). Table 1 shows that the survey respondents predominantly consist of women, accounting for 62.8% of the total sample. This indicates that women are more likely to participate in the survey compared to men. Furthermore, the age range of the respondents is primarily concentrated between 18 to 30 years old, making up 91.5% of the sample. This suggests that the survey attracted a younger demographic, possibly indicating that the topic or nature of the survey was more relevant or appealing to individuals within this age group. In terms of educational background, a significant majority of the respondents possess a university-level education, comprising 77.8% of the sample. This indicates that the survey attracted individuals with higher levels of education, potentially implying that the survey topic required a certain level of knowledge or expertise. Additionally, the data reveals that a large proportion of the respondents have less than one year of work experience, accounting for 66.9% of the sample. This suggests that the survey attracted individuals who are relatively new to the workforce, potentially indicating that the survey topic was more relevant to individuals in the early stages of their careers.
4.1. Factor analysis
Six factors came out after factor analysis with loadings ranging from .43 to .89. Two of the items were eliminated because of cross-loading. The Cronbach’s alpha values ranged from 0.70 to 0.83, calculated using maximum likelihood. Principal Component Analysis was used for the extraction method and Varimax with Kaiser Normalization was employed for rotation. Eigenvalues greater than 1 were observed and cases with missing values were excluded listwise. Overall, Cronbach’s alpha met the criteria for internal consistency of the scale (Hair et al., 2019). The variables from factor analysis are combined into summated scores, subsequently utilized in correlation analysis to examine the relationships between the hypothesized constructs and assess their level of significance.
4.2. Correlation analysis.
Table 2 provides detailed information about the correlation analysis. All the means ranged from 1.49 to 1.97 and fell within the possible values of 1 to 3. Similarly, the standard deviations varied from .82 to .96. Second, all the correlations among the variables were positive, for there were no reversed scales in the questionnaire. The statistical significance of the correlation at the 0.01 level supports the hypotheses about the positive relationships between social network loyalty (SNL) and affect-based trust (ABT, H1a), cognition-based trust (CBT, H2a), and institution-based trust (IBT, H3a). This indicates a robust and noteworthy correlation exists between loyalty on social networks and trust based on affect, trust based on cognition, and trust based on institutions.
4.3. Chi-square analysis
Table 3 reports the results of Crosstabulation. The findings indicate that affect-based trust is highly valued by a significant proportion of Chinese customers, specifically 49.5%, with a statistically significant level of p<.005. In contrast, only 20.7% of American customers share this perspective. Moreover, the data reveals that Chinese customers constitute the majority of the sample, representing 66.2%, while American customers make up 33.8% of the total. This finding suggests that there are distinct cultural differences in the way Chinese and American customers perceive and place trust in others based on emotions and feelings. The significance level of .005 supports the idea that there is a genuine disparity in affect-based trust between these two groups (H1b).
Table 4 reports the results of Chi-square tests. The Pearson Chi-square value of 41.231, indicating statistical significance at the .000 level, supports the claim of a significant difference in the importance of affect-based trust between Chinese and American customers (H1b). Chinese customers prioritize affect-based trust more (49.5%) compared to American customers (20.7%), showing a clear disparity in beliefs.
Chi-square analysis for cognition-based trust. Chinese customers show a significant difference in their belief in cognition-based trust compared to American customers, with statistical significance at .000 (H2b). Specifically, 31.9% of Chinese customers consider cognition-based trust important at p<.005, while only 18.0% of American customers share this view. Furthermore, the data indicates that Chinese customers represent 64.7% of the sample, while American customers account for 35.3%. See Table 2 for more detailed information about the results of Crosstabulation. The Pearson Chi-square value of 39.46 indicates a significant (p<005) difference between Chinese and American customers regarding cognition-based trust (H2b). Chinese customers exhibit a greater level of trust (31.9%) in comparison to American customers (18.0%). On the other hand, American customers tend to have less faith in cognition-based trust and may prioritize other factors rather than cognition-based trust. See Table 3 for more detailed information about the results of Chi-square tests.
Chi-square analysis for institution-based trust. Chinese and American customers differ in institution-based trust, which is statistically significant at .05. Among the Chinese customers surveyed, 47.1% acknowledged the significance of institution-based trust. This indicates that a substantial proportion of Chinese customers recognize the importance of trust in institutions. On the other hand, only 24.8% of American customers shared the same belief, highlighting a considerable contrast in their perception of institution-based trust (H3b). Furthermore, the data also provides insights into the composition of the sample. Chinese customers constituted the majority, accounting for 65.8% of the total sample. In contrast, American customers made up only 34.2% of the sample, suggesting a smaller representation of this group in the study. See Table 2 for more detailed information about the results of Crosstabulation. With a Pearson Chi-square value of 9.021 and a significance level of.05, this finding suggests that Chinese customers, with a proportion of 47.1%, have a higher inclination to believe that institution-based trust leads to loyalty compared to American customers, who only have a proportion of 24.8% (H3b). Additionally, there is a noticeable trend among American customers to have less faith in the institution-based trust leading to loyalty. See Table 3 for more detailed information about the results of Chi-square tests.
Overall, there are statistically significant differences between Chinese customers and American customers regarding affect-based trust (p<.000), cognition-based trust (p<.000), and institution-based trust (p<.011). This finding supports the hypotheses about Chinese and American customers differ in affect-based trust (H1b), cognition-based trust (H2b), and institution-based trust (H3b). Chinese customers tend to prioritize affect-based trust, based on emotions and personal connections, while American customers prioritize cognition-based trust, based on rational assessments. Chinese customers also place more emphasis on institution-based trust, trusting organizations and institutions more than individual actors.
5. Discussion and conclusion
The findings of this study indicate a robust and noteworthy correlation exists between social network loyalty and affect-based trust, cognition-based trust, and institution-based trust. The strong correlations between social network loyalty and trust formed through affect, cognition, and institutions indicate that individuals who have a higher level of trust in social networks are more likely to be seen in various forms, such as frequent usage, active engagement, and positive word-of-mouth recommendations. The connection between social network loyalty and trust based on affect suggests that individuals who have positive emotional experiences on social networks are more likely to trust these networks. This emotional attachment can be fostered through various means, such as personalized content, social support, and a sense of belonging. When individuals feel emotionally connected to a social network, they are more likely to trust it. Similarly, the connection between loyalty and trust based on cognition suggests that individuals who perceive social networks as reliable, credible, and useful are more likely to trust these networks. This perception can be influenced by factors such as the accuracy of information, the transparency of algorithms, and the effectiveness of privacy settings. Additionally, the connection between loyalty and trust based on institutions suggests that individuals who perceive social networks as socially responsible, ethical, and accountable are more likely to trust these platforms. This perception can be influenced by factors such as the social network’s commitment to user privacy, its handling of controversial content, and its response to societal issues.
Overall, these findings highlight the importance of trust in fostering loyalty on social networks. By understanding the factors that contribute to trust, social network providers can enhance user loyalty and engagement. This can be achieved through strategies that focus on creating positive emotional experiences, ensuring reliable and credible information, and demonstrating social responsibility. The findings also highlight the different perspectives of Chinese and American consumers on social network loyalty and affect-based trust, cognition-based trust, and institution-based trust.
Affect-based trust. Chinese and American customers exhibit contrasting attitudes towards affect-based trust. Trust in Chinese culture is primarily established through personal relationships and affiliations. Chinese consumers place great importance on loyalty, collaboration, endorsements from reliable individuals, and personal bonds in their social circles. This finding corroborates the existing literature that trust is built through emotional connections between people and harmony with others (Chu et al., 2021; Chua et al., 2008; Ozpamuk et al., 2023). On the other hand, individualism and independence are highly valued in American culture, leading to a reliance on rational assessments and objective criteria to determine trustworthiness. Recognizing and adapting to the cultural differences in trust between Chinese and American consumers is crucial for businesses to build trust and connect with their target market.
Cognition-based trust is based on rational assessment (Chua et al., 2008; Mcallister, 1995). Chinese customers tend to place a greater emphasis on cognition-based trust and their trust in a product, service, or brand is built on information, evidence, and logical thinking. They research, seek advice, and scrutinize product details before purchasing. They believe that if people compare the price of something at three different locations, they will never be ripped off (huo bi san jia bu chi kui). Moreover, differences in consumer protection measures and regulations in China and the US impact consumer trust. Having experienced fraud and counterfeit goods, Chinese consumers are cautious and rely on cognition-based trust. American consumers, trusting the legal system and regulations, rely less on cognition-based trust. Cultural values, individualism, life experiences, and the regulatory environment all contribute to these differences.
The trust that buyers have in the institution can significantly impact their transaction behavior (Pavlou & Gefen, 2004). Chinese and American consumers display distinct levels of trust impacted by institutional factors. In China, institutions like government agencies and large corporations are often perceived as more trustworthy and reliable when compared to individual actors. In contrast, American customers may be more skeptical of institutions and place greater trust in individual actors and personal relationships.
Implications-limitations and future research. Overall, these differences in trust preferences between Chinese and American customers have significant implications for businesses operating in both markets. Businesses must understand and cater to the unique preferences of these consumer groups. By recognizing and adapting to these cultural differences, companies can effectively build trust and stronger relationships with their target markets and improve satisfaction and loyalty (Chu et al., 2018, 2021). In this study, Chinese customers outnumber American customers in the sample, thus, the results need to be interpreted with caution. Future research will be needed to explore the underlying factors that contribute to this variation and how it affects the relationships and interactions between different cultures (Chu et al., 2005). Additionally, it could be beneficial to evaluate the effectiveness of using affect-based trust, cognition-based trust, and institution-based trust as indicators of loyalty in social networks through techniques like regression analysis or structural equation modeling. Regression analysis can assess the extent to which affect-based trust, cognition-based trust, and institution-based trust predict loyalty, while structural equation modeling can provide a more comprehensive understanding of the complex relationships between these variables. By examining the relationships between trust and loyalty in social networks, researchers can gain insights into the mechanisms that drive loyalty and how trust plays a role in fostering strong connections between individuals and cultures. This knowledge can be valuable for organizations and policymakers seeking to build and maintain strong relationships with diverse communities. Additionally, understanding the factors that contribute to cultural variation can help promote cultural understanding and facilitate effective communication and collaboration between different cultures.